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• W4280497506 abstract "Salvia grossheimii is a perennial herb with antidiabetic and cytotoxic constituents. In continuation of our study on S. grosshiemii to identify the bioactive phytochemicals, we have reported the characterization of seven undescribed triterpenoids. The aerial parts of the plant were extracted in dichloromethane and its constituents were isolated using chromatography techniques. The structures of compounds were identified using 1D, 2D NMR, and ESI-MS spectral data. Seven new oleanane- and ursane-type triterpenoids (1–7) were identified in S. grossheimii. The structures of 1–7 were characterized as; 2α-hydroxy-3β-acetoxy-olean-9(11),12-diene (1), 2α-acetoxy-3β-hydroxy-olean-9(11),12-diene (2), 3β-acetoxy-olean-18-ene,2α,11α-diol (3), 2α-hydroxy-3β-acetoxy-urs-9(11),12-diene (4), 2α-acetoxy-3β-hydroxy-urs-9(11),12-diene (5), 2α,3β-diacetoxy-urs-12-ene-11α,20β-diol (6), 2α,3β-diacetoxy-urs-9(11),12-diene-20β-ol (7). Triterpenoids (2, 5, and 7) were intramolecular transesterification or dehydration products of their corresponding isomers or allylic alcohol in the C rings, respectively, produced in-situ during NMR spectroscopy. Virtual screening of 1–7 was performed with molecular docking analysis to identify the potential SARS-CoV-2 and α-glucosidase inhibitors using the smina molecular docking algorithm. The best binding energy values (kcal/mol) against COVID-19 main protease Mpro were calculated for 6 (-8.77) and 7 (-8.68), and the higher binding affinities toward human α-glucosidase were obtained for 2 (-9.39) and 6 (-8.63). This study suggests S. grossheimii as a rich source of bioactive triterpenoids and introduces new natural compounds. Considering the high binding energy values of 2, 6, and 7, these structures could be candidates for anti-COVID-19 and antidiabetic drug development in the future." @default.
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• W4280497506 date "2022-05-11" @default.
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• W4280497506 title "Anti-COVID-19 and antidiabetic activities of new oleanane and ursane-type triterpenoids from <i>Salvia grossheimii</i>: an <i>in-silico</i> approach" @default.
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• W4280497506 doi "https://doi.org/10.1080/10799893.2022.2072891" @default.
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